Identifying Weight-Variant Latent Causal Models

Understand

The task of causal representation learning aims to uncover latent higher-level causal variables that affect lower-level observations.

• Identifying the true latent causal variables from observed data, while allowing instantaneous causal relations among latent variables, remains a challenge, however.
• To this end, we start with the analysis of three intrinsic indeterminacies in identifying latent variables from observations: transitivity, permutation indeterminacy, and scaling indeterminacy.
• We find that transitivity acts as a key role in impeding the identifiability of latent causal variables.